Although collaborative web-based tools are often used in blended environments such as education, little research has analysed the predictive power of face-to-face social connections on measurable user behaviours in online collaboration, particularly in diverse settings. In this paper, we use Social Network Analysis to compare users’ pre-existing social networks with the quantity of their contributions to an online chat-based collaborative activity in a higher education classroom. In addition, we consider whether the amount of diversity present in one’s social network leads to more online contributions in an anonymous cross-cultural collaborative setting. Our findings indicate that pre-existing social connections can predict how much users contribute to online education-related collaborative activities with diverse group members, even more so than academic performance. Furthermore, our findings suggest that future Web Science research should consider how the more traditionally ‘qualitative’ socio-cultural influences affect user participation and use of online collaborative tools. Paper: http://oro.open.ac.uk/46221/

1. Using Social Network Analysis
to predict online contributions:
The impact of network diversity on cross-
cultural collaboration
Jenna Mittelmeier
Institute of Educational Technology
The Open University, UK
Co-Authors: Yingfei Heliot (University of Surrey)
Bart Rienties (The Open University)
Denise Whitelock (The Open University)
@JLMittelmeier

2. @JLMittelmeier

3. ?
Strijbos & de Laat, 2010
Xing, Wadholm & Goggins, 2014
Caspi, Gorsky & Chajut, 2003
@JLMittelmeier

4. Free-rider:
‘contributes to the group task when explicitly
prompted but minimises the effort as much
as possible; often leading to substandard
contributions’ (Strijbos & de Laat, 2010)
#1 complaint of students in cross-cultural group work
(Popov et al, 2012)
@JLMittelmeier

5. Cohort-like classrooms
(Kimmel & Volet, 2012)
Previous multicultural
experiences
(Summers & Volet, 2008)
Cultural understanding
and respect
(Woods, Barker & Hibbins, 2010)
@JLMittelmeier

6. Research Questions
RQ1: How do users’ social networks within a face-to-
face environment influence the quantity of contributions
when they collaborate online?
RQ2: To what extent do users’ positions within their
social network in a face-to-face setting predict their
behaviours when they collaborate online?

7. Setting
118 student classroom
92% international students from 24 countries
Physical class session, optional online synchronous
collaborative activities
Research Method
Activity: working anonymously in groups of five for
one hour
Social Network Analysis surveys compared with data
from online collaborative tool

8. @JLMittelmeier

9. -1 0 1
EI Index

10. Network Density
# of social connections
total # of possible
social connections
@JLMittelmeier

11. Contribution data analysed
# of posts
submitted
summed word
count
submitted
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12. Activity participation –
Bivariate Analysis
58 Attendees from 13 countries
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13. Contribution Quantity –
Bivariate Analysis
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14. Contribution Quantity –
Regression Analysis
Number of
Posts Submitted
18.9% of variation explained by:
EI Index
(β= .388,
p = .003)
Social Network
Density
(β=.252,
p = .044)
Summed Word
Count Submitted
16.0% of variation explained
by:
EI Index
(β= .406,
p = .002)
@JLMittelmeier

15. Research Questions
RQ1: How do users’ social networks within a face-to-
face environment influence the quantity of contributions
when they collaborate online?
RQ2: To what extent do users’ positions within their
social network in a face-to-face setting predict their
behaviours when they collaborate online?
@JLMittelmeier

16. What does this mean?
@JLMittelmeier

17. Implications for Web Science
1.) The physical social space has a clear linkage to
online behaviours in blended environments
2.) Traditionally ‘quantifiable’ data may not capture
all influences on human behaviours
3.) ‘Social agency’ as a factor for participation
@JLMittelmeier

18. Sources
• Caspi, A., Gorsky, P., & Chajut, E. (2003). The influence of group size on nonmandatory
asynchronous instructional disscussion groups. Internet and Higher Education, 6, 227-240.
• Kimmel, K., & Volet, S. (2012). University students' perceptions of and attitudes towards culturally
diverse group work: Does context matter? Journal of Studies in International Education, 16(2),
157-181.
• Popov, V., Brinkman, D., Biemans, H. J. A., Mulder, M., Kuznetsov, A., & Noroozi, O. (2012).
Multicultural student group work in higher education: An explorative case study on challenges as
perceive by students. International Journal of Intercultural Relations, 36, 302-317.
• Strijbos, J.-W., & de Laat, M. F. (2010). Developing the role concept for computer-supported
collaborative learning: An explorative synthesis. Computers in Human Behavior, 26, 495-505.
• Summers, M., & Volet, S. (2008). Students' attitudes towards culturally mixed groups on
international campuses: Impact of participation in diverse and non-diverse groups. Studies in
Higher Education, 33(4), 357-370.
• Woods, P., Barker, M., & Hibbins, R. (2010). Tapping the benefits of multicultural group work: An
exploratory study of postgraduate management students. International Journal of Educational
Management, 9(2), 59-70.
• Xing, W., Wadholm, B., & Goggins, S. (2014). Learning analytics in CSCL with a focus on
assessment: An exploratory study of activity theory-informed cluster analysis. Paper presented at
the Fourth International Conference on Learning Analytics and Knowledge, Indianapolis, USA.